No solutions in slides

_freeze/assignments/A1/execute-results/html.json

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     "markdown": "---\ntitle: Assignment 1\n---\n\n\n**You must provide forecasts for the following items:**\n\n  1. Google closing stock price on 20 March 2024 [[Data](https://finance.yahoo.com/quote/GOOG/)].\n  2. Maximum temperature at Melbourne airport on 10 April 2024 [[Data](http://www.bom.gov.au/climate/dwo/IDCJDW3049.latest.shtml)].\n  3. The difference in points (Collingwood minus Essendon) scored in the AFL match between Collingwood and Essendon for the Anzac Day clash. 25 April 2024 [[Data](https://en.wikipedia.org/wiki/Anzac_Day_match)].\n  4. The seasonally adjusted estimate of total employment for April 2024. ABS CAT 6202, to be released around mid May 2024 [[Data](https://www.abs.gov.au/statistics/labour/employment-and-unemployment/labour-force-australia/latest-release)].\n  5. Google closing stock price on 22 May 2024 [[Data](https://finance.yahoo.com/quote/GOOG/)].\n\n**For each of these, give a point forecast and an 80% prediction interval, and explain in a couple of sentences how each was obtained.**\n\n* The [Data] links give you possible data to start with, but you are free to use any data you like.\n* There is no need to use any fancy models or sophisticated methods. Simple is better for this assignment. The methods you use should be understandable to any high school student.\n* Full marks will be awarded if you submit the required information, and are able to meaningfully justify your results in a couple of sentences in each case.\n* Once the true values in each case are available, we will come back to this exercise and see who did the best using the scoring method described in class.\n* The student with the lowest score is the winner of our forecasting competition, and will win a $50 cash prize.\n* The assignment mark is not dependent on your score.\n\n\n<br><br><hr><b>Due: 8 March 2024</b><br><a href=https://learning.monash.edu/mod/quiz/view.php?id=2298116 class = 'badge badge-large badge-blue'><font size='+2'>&nbsp;&nbsp;<b>Submit</b>&nbsp;&nbsp;</font><br></a>\n",
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_freeze/index/execute-results/html.json

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     "markdown": "---\ntitle: \"ETC3550/5550 Applied forecasting\"\n---\n\n\n\n\n## Teaching team\n### Lecturer/Chief Examiner\n\n\n:::: {.columns}\n\n::: {.column width=\"25%\"}\n\n![](https://robjhyndman.com/img/RJH_AAS_small.png){style=\"width: 80%; border-radius: 50%;\"}\n:::\n\n::: {.column width=\"75%\"}\n\n[**Rob J Hyndman**](https://robjhyndman.com)\n\nEmail: [[email protected]](mailto:[email protected])\n\n:::\n\n::::\n\n### Head Tutor\n\n:::: {.columns}\n\n::: {.column width=\"25%\"}\n\n![](https://avatars.githubusercontent.com/u/16127127?v=4){style=\"width: 80%;border-radius: 50%;\"}\n:::\n\n::: {.column width=\"75%\"}\n\n[**Mitchell O'Hara-Wild**](https://mitchelloharawild.com)\n\nEmail: [[email protected]](mailto:[email protected])\n\n:::\n\n::::\n\n### Tutors\n\n* Elena Sanina\n* Zhixiang (Elvis) Yang\n* Jarryd Chapman\n* Xiefei (Sapphire) Li\n* Xiaoqian Wang\n\n## Weekly schedule\n\n* [Pre-recorded videos](https://www.youtube.com/watch?v=uwKiT1o1TkI&list=PLyCNZ_xXGzpm7W9jLqbIyBAiSO5jDwJeE): approximately 1 hour per week [[Slides](https://github.com/robjhyndman/fpp3_slides)]\n* Tutorials: 1.5 hours in class per week\n* Seminars: 9am Fridays, [Central 1 Lecture Theatre, 25 Exhibition Walk](https://maps.app.goo.gl/RKdmJq2tBfw8ViNT9).\n\n\n\n\n|Week   |Topic                               |Chapter                          |Assessments    |\n|:------|:-----------------------------------|:--------------------------------|:--------------|\n|26 Feb |[Introduction to forecasting and R](./week1/index.html)|[1. Getting started](https://OTexts.com/fpp3/intro.html)|               |\n|04 Mar |[Time series graphics](./week2/index.html)|[2. Time series graphics](https://OTexts.com/fpp3/graphics.html)|[Assignment 1](assignments/A1.qmd)|\n|11 Mar |[Time series decomposition](./week3/index.html)|[3. Time series decomposition](https://OTexts.com/fpp3/decomposition.html)|               |\n|18 Mar |[The forecaster's toolbox](./week4/index.html)|[5. The forecaster's toolbox](https://OTexts.com/fpp3/toolbox.html)|[Assignment 2](assignments/A2.qmd)|\n|25 Mar |[Exponential smoothing](./week5/index.html)|[8. Exponential smoothing](https://OTexts.com/fpp3/expsmooth.html)|               |\n|01 Apr |Mid-semester break                  |                                 |               |\n|08 Apr |[Exponential smoothing](./week6/index.html)|[8. Exponential smoothing](https://OTexts.com/fpp3/expsmooth.html)|[Assignment 3](assignments/A3.qmd)|\n|15 Apr |[ARIMA models](./week7/index.html)  |[9. ARIMA models](https://OTexts.com/fpp3/arima.html)|               |\n|22 Apr |[ARIMA models](./week8/index.html)  |[9. ARIMA models](https://OTexts.com/fpp3/arima.html)|               |\n|29 Apr |[ARIMA models](./week9/index.html)  |[9. ARIMA models](https://OTexts.com/fpp3/arima.html)|[Assignment 4](assignments/A4.qmd)|\n|06 May |[Multiple regression and forecasting](./week10/index.html)|[7. Time series regression models](https://OTexts.com/fpp3/regression.html)|               |\n|13 May |[Dynamic regression](./week11/index.html)|[10. Dynamic regression models](https://OTexts.com/fpp3/dynamic.html)|               |\n|20 May |[Dynamic regression](./week12/index.html)|[10. Dynamic regression models](https://OTexts.com/fpp3/dynamic.html)|[Retail Project](assignments/Project.qmd)|\n\n\n## Assessments\n\n* [Assignment 1](assignments/A1.qmd): 2%\n* [Assignment 2](assignments/A2.qmd): 6%\n* [Assignment 3](assignments/A3.qmd): 6%\n* [Assignment 4](assignments/A4.qmd): 6%\n* [Retail project](assignments/Project.qmd): 20%\n* Final exam: 60%\n\n## R package installation\n\nHere is the code to install the R packages we will be using in this unit.\n\n```r\ninstall.packages(c(\"tidyverse\",\"fpp3\", \"GGally\"), dependencies = TRUE)\n```\n",
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_freeze/week1/index/execute-results/html.json

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-    "markdown": "---\ntitle: \"Week 1: What is forecasting?\"\n---\n\n::: {.cell}\n\n:::\n\n\n## What you will learn this week\n\n* How to think about forecasting from a statistical perspective\n* What makes something easy or hard to forecast?\n* Using the `tsibble` package in R\n\n## Pre-class activities\n\n* Install R and RStudio on your personal computer. Instructions are provided at [https://otexts.com/fpp3/appendix-using-r.html](https://otexts.com/fpp3/appendix-using-r.html).\n* Read [Chapter 1 of the textbook](http://OTexts.com/fpp3/intro.html) and watch all embedded videos\n* Watch this video\n\n<iframe width=\"100%\" height=\"415\" src=\"https://www.youtube.com/embed/HNJYRf0mvxg?si=k0wfI3Sq68TPm4Ek\" title=\"YouTube video player\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" allowfullscreen></iframe>\n\n## Exercises (on your own or in tutorial)\n\nYour task this week is to make sure you are familiar with R, RStudio and the tidyverse packages. If you've already done ETC1010, then you may not need to do anything! But if you're new to R and the tidyverse, then you will need to get yourself up-to-speed.\n\nWork through the first five chapters of the **LearnR** tutorial at [learnr.numbat.space](https://learnr.numbat.space). Do as much of it as you think you need. For those students new to R, it is strongly recommended that you do all five chapters. For those who have previously used R, concentrate on the parts where you feel you are weakest.\n\n\n## Slides for seminar\n\n<iframe src='https://docs.google.com/gview?url=https://af.numbat.space/week1/slides.pdf&embedded=true' width='100%' height=465></iframe>\n<a href=https://af.numbat.space/week1/slides.pdf class='badge badge-small badge-red'>Download pdf</a>\n\n## Seminar activities\n\n\n1. Download `tourism.xlsx` from [`http://robjhyndman.com/data/tourism.xlsx`](http://robjhyndman.com/data/tourism.xlsx), and read it into R using `read_excel()` from the `readxl` package.\n2. Create a tsibble which is identical to the `tourism` tsibble from the `tsibble` package.\n3. Find what combination of `Region` and `Purpose` had the maximum number of overnight trips on average.\n4. Create a new tsibble which combines the Purposes and Regions, and just has total trips by State.\n\n\n\n## Assignments\n\n* [Assignment 1](../assignments/A1.qmd) is due on Friday 08 March.\n",
+    "markdown": "---\ntitle: \"Week 1: What is forecasting?\"\n---\n\n::: {.cell}\n\n:::\n\n\n## What you will learn this week\n\n* How to think about forecasting from a statistical perspective\n* What makes something easy or hard to forecast?\n* Using the `tsibble` package in R\n\n## Pre-class activities\n\n* Install R and RStudio on your personal computer. Instructions are provided at [https://otexts.com/fpp3/appendix-using-r.html](https://otexts.com/fpp3/appendix-using-r.html).\n* Read [Chapter 1 of the textbook](http://OTexts.com/fpp3/intro.html) and watch all embedded videos\n* Watch this video\n\n<iframe width=\"100%\" height=\"415\" src=\"https://www.youtube.com/embed/HNJYRf0mvxg?si=k0wfI3Sq68TPm4Ek\" title=\"YouTube video player\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" allowfullscreen></iframe>\n\n## Exercises (on your own or in tutorial)\n\nYour task this week is to make sure you are familiar with R, RStudio and the tidyverse packages. If you've already done ETC1010, then you may not need to do anything! But if you're new to R and the tidyverse, then you will need to get yourself up-to-speed.\n\nWork through the first five chapters of the **LearnR** tutorial at [learnr.numbat.space](https://learnr.numbat.space). Do as much of it as you think you need. For those students new to R, it is strongly recommended that you do all five chapters. For those who have previously used R, concentrate on the parts where you feel you are weakest.\n\n\n## Slides for seminar\n\n<iframe src='https://docs.google.com/gview?url=https://af.numbat.space/week1/slides.pdf&embedded=true' width='100%' height=465></iframe>\n<a href=https://af.numbat.space/week1/slides.pdf class='badge badge-small badge-red'>Download pdf</a>\n\n## Seminar activities\n\n\n1. Download `tourism.xlsx` from [`http://robjhyndman.com/data/tourism.xlsx`](http://robjhyndman.com/data/tourism.xlsx), and read it into R using `read_excel()` from the `readxl` package.\n2. Create a tsibble which is identical to the `tourism` tsibble from the `tsibble` package.\n3. Find what combination of `Region` and `Purpose` had the maximum number of overnight trips on average.\n4. Create a new tsibble which combines the Purposes and Regions, and just has total trips by State.\n\n<a href=https://af.numbat.space/week1/solutions.R class='badge badge-small badge-green'>Solutions</a>\n\n\n## Assignments\n\n* [Assignment 1](../assignments/A1.qmd) is due on Friday 08 March.\n",
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